Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions

Experimental and simulation-based characterization of surfactant adsorption layers at fluid interfaces

Adsorption of surfactants to fluid interfaces occurs in numerous technological and daily-life contexts. The coverage at the interface and other properties of the formed adsorption layers determine the performance of a surfactant with regard to the desired application. Given the importance of these applications, there is a great demand for the comprehensive characterization and understanding of surfactant adsorption layers. In this review, we provide an overview of suitable experimental and simulation-based techniques and review the literature in which they were used for the investigation of surfactant adsorption layers. We come to the conclusion that, while these techniques have been successfully applied to investigate Langmuir monolayers of water-insoluble surfactants, their application to the study of Gibbs adsorption layers of water-soluble surfactants has not been fully exploited. Finally, we emphasize the great potential of these methods in providing a deeper understanding of the behavior of soluble surfactants at interfaces, which is crucial for optimizing their performance in various applications.

A comprehensive review on sustainable surfactants from CNSL: chemistry, key applications and research perspectives

Surfactants, a group of amphiphilic molecules (i.e. with hydrophobic(water insoluble) as well as hydrophilic(water soluble) properties) can modulate interfacial tension. Currently, the majority of surfactants depend on petrochemical feedstocks (such as oil and gas). However, deployment of these petrochemical surfactants produces high toxicity and also has poor biodegradability which can cause more environmental issues. To address these concerns, the current research is moving toward natural resources to produce sustainable surfactants. Among the available natural resources, Cashew Nut Shell Liquid (CNSL) is the preferred choice for industrial scenarios to meet their goals of sustainability. CNSL is an oil extracted from non-edible cashew nut shells, which doesn't affect the food supply chain. The unique structural properties and diverse range of use cases of CNSL are key to developing eco-friendly surfactants that replace petro-based surfactants. Against this backdrop, this article discusses various state-of-the-art developments in key cardanol-based surfactants such as anionic, cationic, non-ionic, and zwitterionic. In addition to this, the efficiency and characteristics of these surfactants are also analyzed and compared with those of the synthetic surfactants (petro-based). Furthermore, the present paper also focuses on various market aspects and different applications in various industries. Finally, this article describes various future research perspectives including Artificial Intelligence technology which, of late, is having a huge impact on society.

Determining interfacial tension and critical micelle concentrations of surfactants from atomistic molecular simulations

Hypothesis Atomistically-detailed models of surfactants provide quantitative information on the molecular interactions and spatial distributions at fluid interfaces. Hence, it should be possible to extract from this information, macroscopical thermophysical properties such as interfacial tension, critical micelle concentrations and the relationship between these properties and the bulk fluid surfactant concentrations. Simulations and Experiments Molecular-scale interfacial of systems containing n-dodecyl β-glucoside (APG12) are simulated using classical molecular dynamics. The bulk phases and the corresponding interfacial regions are all explicitly detailed using an all-atom force field (PCFF+). During the simulation, the behaviour of the interface is analyzed geometrically to obtain an approximated value of the critical micelle concentration (CMC) in terms of the surfactant area number density and the interfacial tension is assessed through the analysis of the forces amongst molecules. New experimental determinations are reported for the surface tension of APG12 at the water/air and at the water/n-decane interfaces. Findings We showcase the application of a thermodynamic framework that inter-relates interfacial tensions, surface densities, CMCs and bulk surfactant concentrations, which allows the in silico quantitative prediction of interfacial tension isotherms.

Computational predictions of interfacial tension, surface tension, and surfactant adsorption isotherms

All-atom (AA) molecular dynamics (MD) simulations are employed to predict interfacial tensions (IFT) and surface tensions (ST) of both ionic and non-ionic surfactants. The general AMBER force field (GAFF) and variants are examined in terms of their performance in predicting accurate IFT/ST, γ, values for chosen water models, together with the hydration free energy, ΔGhyd, and density, ρ, predictions for organic bulk phases. A strong correlation is observed between the quality of ρ and γ predictions. Based on the results, the GAFF-LIPID force field, which provides improved ρ predictions is selected for simulating surfactant tail groups. Good γ predictions are obtained with GAFF/GAFF-LIPID parameters and the TIP3P water model for IFT simulations at a water-triolein interface, and for GAFF/GAFF-LIPID parameters together with the OPC4 water model for ST simulations at a water-vacuum interface. Using a combined molecular dynamics-molecular thermodynamics theory (MD-MTT) framework, a mole fraction of C12E6 molecule of 1.477 × 10-6 (from the experimental critical micelle concentration, CMC) gives a simulated surface excess concentration, ΓMAX, of 76 C12E6 molecules at a 36 nm2 water-vacuum surface (3.5 × 10-10 mol cm-2), which corresponds to a simulated ST of 35 mN m-1. The results compare favourably with an experimental ΓMAX of C12E6 of 3.7 × 10-10 mol cm-2 (80 surfactants for a 36 nm2 surface) and experimental ST of C12E6 of 32 mN m-1 at the CMC.

Interface Adsorption versus Bulk Micellization of Surfactants: Insights from Molecular Simulations

Surfactants play essential roles in many commonplace applications and industrial processes. Although significant progress has been made over the past decades with regard to model-based predictions of the behavior of surfactants, important challenges have remained. Notably, the characteristic time scales of surfactant exchange among micelles, interfaces, and the bulk solution typically exceed the time scales currently accessible with atomistic molecular dynamics (MD) simulations. Here, we circumvent this problem by introducing a framework that combines the general thermodynamic principles of self-assembly and interfacial adsorption with atomistic MD simulations. This approach provides a full thermodynamic description based on equal chemical potentials and connects the surfactant bulk concentration, the experimental control parameter, with the surfactant surface density, the suitable control parameter in MD simulations. Self-consistency is demonstrated for the nonionic surfactant C12EO6 (hexaethylene glycol monododecyl ether) at an alkane/water interface, for which the adsorption and pressure isotherms are computed. The agreement between the simulation results and experiments is semiquantitative. A detailed analysis reveals that the used atomistic model captures well the interactions between surfactants at the interface but less so their adsorption affinities to the interface and incorporation into micelles. Based on a comparison with other recent studies that pursued similar modeling challenges, we conclude that the current atomistic models systematically overestimate the surfactant affinities to aggregates, which calls for improved models in the future.

Molecularly informed field theory for estimating critical micelle concentrations of intrinsically disordered protein surfactants

The critical micelle concentration (CMC) is a crucial parameter in understanding the self-assembly behavior of surfactants. In this study, we combine simulation and experiment to demonstrate the predictive capability of molecularly informed field theories in estimating the CMC of biologically based protein surfactants. Our simulation approach combines the relative entropy coarse-graining of small-scale atomistic simulations with large-scale field-theoretic simulations, allowing us to efficiently compute the free energy of micelle formation necessary for the CMC calculation while preserving chemistry-specific information about the underlying surfactant building blocks. We apply this methodology to a unique intrinsically disordered protein platform capable of a wide variety of tailored sequences that enable tunable micelle self-assembly. The computational predictions of the CMC closely match experimental measurements, demonstrating the potential of molecularly informed field theories as a valuable tool to investigate self-assembly in bio-based macromolecules systematically.

Surfactant Adsorption Layers: Experiments and Modeling

During recent years, great progress has been made in understanding the adsorption of surfactants at liquid interfaces. In addition to tensiometry, new efficient methodologies have been developed, in particular interfacial selective optical methods which allow direct access to the adsorbed amounts and interfacial layer compositions. In addition to these new experimental tools, the thermodynamic description by equations of state now allows one to provide a quantitative picture of surfactant interfacial layers. This is most notable for surfactant layers at water/oil interfaces. Additional knowledge about the structure of interfacial layers was gained through different types of molecular modeling. Improved interrelationships between these three aspects are the challenges for current and future work. Particular attention must be paid to dilational interfacial rheology studies, as these mechanical quantities are much more sensitive to small changes in the interfacial composition and structure.

Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution

HYPOTHESIS

Predicting the surface tension (SFT)-log(c) profiles of hydrocarbon surfactants in aqueous solution is computationally non-trivial, and empirically challenging due to the diverse and complex architecture and interactions of surfactant molecules. Machine learning (ML), combining a data-based and knowledge-based approach, can provide a powerful means to relate molecular descriptors to SFT profiles.

EXPERIMENTS

A dataset of SFT for 154 model hydrocarbon surfactants at 20-30 °C is fitted to the Szyszkowski equation to extract three characteristic parameters (Γ_max,K_L and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (∼10) descriptors were selected by removing co-correlation, and employing a gradient-boosted regressor model to rank feature importance and carry out recursive feature elimination (RFE). The hyperparameters of each target-variable model were fine-tuned using a randomised cross-validated grid search, to improve predictive ability and reduce overfitting.

FINDINGS

The ML models correlate favourably with test experimental data, with R²= 0.69-0.87, and the merits and limitations of the approach are discussed based on 'unseen' hydrocarbon surfactants. The incorporation of a knowledge-based framework provides an appropriate smoothing of the experimental data which simplifies the data-driven approach and enhances its generality. Open-source codes and a brief tutorial are provided.

Tuning Contact Angles of Aqueous Droplets on Hydrophilic and Hydrophobic Surfaces by Surfactants

Adsorption of small amphiphilic molecules occurs in various biological and technological processes, sometimes desired while other times unwanted (e.g., contamination). Surface-active molecules preferentially bind to interfaces and affect their wetting properties. We use molecular dynamics simulations to study the adsorption of short-chained alcohols (simple surfactants) to the water–vapor interface and solid surfaces of various polarities. With a theoretical analysis, we derive an equation for the adsorption coefficient, which scales exponentially with the molecular surface area and the surface wetting coefficient and is in good agreement with the simulation results. We apply the outcomes to aqueous sessile droplets containing surfactants, where the competition of surfactant adsorptions to both interfaces alters the contact angle in a nontrivial way. The influence of surfactants is the strongest on very hydrophilic and hydrophobic surfaces, whereas droplets on moderately hydrophilic surfaces are less affected.

Osmotic Method for Calculating Surface Pressure of Monolayers in Molecular Dynamics Simulations

Surface pressure is a fundamental thermodynamic property related to the activity of molecules at interfaces. In molecular simulations, it is typically calculated from its definition: the difference between the surface tension of the air-water and air-surfactant interfaces. In this Letter, we show how to connect the surface pressure with a two-dimensional osmotic pressure and how to take advantage of this analogy to obtain a practical method of calculating surface pressure-area isotherms in molecular simulation. As a proof-of-concept, compression curves of zwitterionic and ionic surfactant monolayers were obtained using the osmotic approach and the curves were compared with the ones from the traditional pressure tensor-based scheme. The results shown an excellent agreement between both alternatives. Advantageously, the osmotic approach is simple to use and allows to obtain the surface pressure-area isotherm on the fly with a single simulation using equilibration stages.

Molecular Simulations of Surfactant Adsorption on Iron Oxide from Hydrocarbon Solvents

The performance of organic friction modifiers (OFMs) depends on their ability to adsorb onto surfaces and form protective monolayers. Understanding the relationship between OFM concentration in the base oil and the resulting surface coverage is important for improving lubricant formulations. Here, we use molecular dynamics (MD) simulations to study the adsorption of three OFMs─stearic acid (SA), glycerol monoostearate (GMS), and glycerol monooleate (GMO)─onto a hematite surface from two hydrocarbon solvents─n-hexadecane and poly(α-olefin) (PAO). We calculate the potential of mean force of the adsorption process using the adaptive biasing force algorithm, and the adsorption strength increases in the order SA < GMS < GMO. We estimate the minimum area occupied by OFM molecules on the surface using annealing MD simulations and obtained a similar hard-disk area for GMS and GMO but a lower value for SA. Using the MD results, we determine the adsorption isotherms using the molecular thermodynamic theory (MTT), which agree well with one previous experimental data set for SA on hematite. For two other experimental data sets for SA, lateral interactions between surfactant molecules need to be accounted for within the MTT framework. SA forms monolayers with lower surface coverage than GMO and GMS at low concentrations but also has the highest plateau coverage. We validate the adsorption energies from the MD simulations using high-frequency reciprocating rig friction experiments with different concentrations of the OFMs in PAO. For OFMs with saturated tailgroups (SA and GMS), we obtain good agreement between the simulations and the experiments. The results deviate for OFMs containing Z-unsaturated tailgroups (GMO) due to the additional steric hindrance, which is not accounted for in the current simulation framework. This study demonstrates that MD simulations, alongside MTT, are an accurate and efficient tool to predict adsorption isotherms at solid-liquid interfaces.

Predicting Critical Micelle Concentrations for Surfactants Using Graph Convolutional Neural Networks

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally-tensiometry-is laborious and expensive. In this study, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. In particular, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy on a more inclusive data set than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants using a single model. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this behavior to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.

Ion Valency and Concentration Effect on the Structural and Thermodynamic Properties of Brine-Decane Interfaces with Anionic Surfactant (SDS)

Salt ion valency and concentration vary in actual oil reservoirs, which play an important role in the functionalities of surfactant formula during chemical flooding processes to enhance oil recovery. Herein, we report a molecular dynamics (MD) study to investigate the ion valency and concentration effect on the structural and thermodynamic properties of brine-decane interfaces with anionic surfactant (SDS), under typical reservoir conditions (353 K and 200 bar). We use two different cations (Na⁺ and Ca²⁺) and a wide range of ion concentrations (up to 3.96 M) to simulate reservoir conditions. We find that ion valency has a significant effect on the molecular configurations, which further influences the thermodynamic properties. Ca²⁺ ions can have a strong adsorption at the interface due to the strong electrostatic interactions between Ca²⁺ ions and SDS, which also results in the Cl^- ion enrichment at the interface. Furthermore, Ca²⁺ ions can form pentagon-like SDS-Ca²⁺ complexes through SDS-Ca²⁺-SDS cation bridging, which renders a nonuniform distribution of SDS at the interface. On the other hand, the cation bridging density monotonically increases as ion concentration increases for the systems without Ca²⁺ ions, while first increases, then decreases for the systems with Ca²⁺ ions. This is because the accumulation of Cl^- ions at the interface at high salt concentrations can melt SDS-Ca²⁺ complexes. This work should provide new insights into the structural and thermodynamic properties of brine-oil interfaces with an anionic surfactant, which can facilitate the optimization of chemical flooding processes.

Contribution of Different Molecules and Moieties to the Surface Tension in Aqueous Surfactant Solutions. II: Role of the Size and Charge Sign of the Counterions

Understanding the role of the counterion species in surfactant solutions is a complicated task, made harder by the fact that, experimentally, it is not possible to vary independently bulk and surface quantities. Here, we perform molecular dynamics simulations at constant surface coverage of the liquid/vapor interface of lithium, sodium, potassium, rubidium, and cesium dodecyl sulfate aqueous solutions. We investigate the effect of counterion type and charge sign on the surface tension of the solution, analyzing the contribution of different species and moieties to the lateral pressure profile. The observed trends are qualitatively compatible with the Hofmeister series, with the notable exception of sodium. We point out a possible shortcoming of what is at the moment, in our experience, the most realistic nonpolarizable force field (CHARMM36) that includes the parametrization for the whole series of alkali counterions. In the artificial system where the counterion and surfactant charges are inverted in sign, the counterions become considerably harder. This charge inversion changes considerably the surface tension contributions of the counterions, surfactant headgroups, and water molecules, stressing the key role of the hardness of the counterions in this respect. However, the hydration free energy gain of the counterions, occurring upon charge inversion, is compensated by the concomitant free energy loss of the headgroups and water molecules, leading to a negligible change in the surface tension of the entire system.

A Multistate Adsorption Model for the Adsorption of C14EO4 and C14EO8 at the Solution/Air Interface

The dynamic and equilibrium properties of adsorption layers of poly (oxyethylene) alkyl ether (CnEOm) can be well described by the reorientation model. In its classical version, it assumes two adsorption states; however, there are obviously surfactants that can adsorb in more than two possible conformations. The experimental data for C14EO4 and C14EO8 (dynamic and equilibrium surface tensions and surface dilational visco-elasticity as measured by bubble profile analysis tensiometry) are used to verify if a reorientation model with more than two possible adsorption states can better describe the complete set data of CnEOm adsorption layers at the water/air interface. The proposed refined theoretical model allows s different states of the adsorbing molecules at the interface. The comparison between the model and experiment demonstrates that, for C14EO4, the assumption of s = 5 adsorption states provides a much better agreement than for s = 2, while for C14EO8, a number of s = 10 adsorption states allows an optimum data description.

Role of the Counterions in the Surface Tension of Aqueous Surfactant Solutions. A Computer Simulation Study of Alkali Dodecyl Sulfate Systems

We have investigated the surface tension contributions of the counterions, surfactant headgroups and tails, and water molecules in aqueous alkali dodecyl sulfate (DS) solutions close to the saturated surface concentration by analyzing the lateral pressure profile contribution of these components using molecular dynamics simulations. For this purpose, we have used the combination of two popular force fields, namely KBFF for the counterions and GROMOS96 for the surfactant, which are both parameterized for the SPC/E water model. Except for the system containing Na+ counterions, the surface tension of the surfactant solutions has turned out to be larger rather than smaller than that of neat water, showing a severe shortcoming of the combination of the two force fields. We have traced back this failure of the potential model combination to the unphysically strong attraction of the KBFF counterions, except for Na+, to the anionic head of the surfactants. Despite this failure of the model, we have observed a clear relation between the soft/hard character (in the sense of the Hofmeister series) and the surface tension contribution of the counterions, which, given the above limitations of the model, can only be regarded as an indicative result. We emphasize that the obtained results, although in a twisted way, clearly stress the crucial role the counterions of ionic surfactants play in determining the surface tension of the aqueous surfactant solutions.

Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces

Although adsorption isotherms of surfactants are critical in determining the relationship between interfacial properties and structures of surfactants, providing quantitative predictions of the isotherms remains challenging. This is especially true for adsorption at hard interfaces such as on two-dimensional (2D) layered materials or on nanoparticles where simulation techniques developed for fluid-fluid interfaces that dynamically change surface properties by adjusting unit cells do not apply. In this work, we predict nonideal adsorption at a solid-solution interface with a molecular thermodynamic theory (MTT) model that utilizes molecular dynamics (MD) simulations for the determination of free-energy parameters in the MTT. Furthermore, the MD/MTT model provides atomistic insights into the nonideal behavior of surfactants by capturing structural phases of the surfactants at the interface. Our approach captures structural transitions from the ideal state at low concentrations and then to the critical surface aggregation concentration (CSAC) and finally through the critical micelle concentration (CMC). We validate our model against the original MTT model by comparing predicted adsorption isotherms of a simplified surfactant system from both approaches. We further substantiate the applicability of our model in complex systems by providing adsorption isotherms in an aqueous sodium dodecyl sulfate (SDS)-graphene system, in good agreement with the experimental observations of the CSAC for the same system.

Exploring the Free Energy Landscape To Predict the Surfactant Adsorption Isotherm at the Nanoparticle-Water Interface

The long-lasting stability of nanoparticle (NP) suspensions in aqueous solution is one of the main challenges in colloidal science. The addition of surfactants is generally adopted to increase the free energy barrier between NPs and hence to ensure a more stable condition avoiding the NP sedimentation. However, a tailored prediction of surfactant concentration enabling a good dispersion of NPs is still an ambitious objective. Here, we demonstrate the efficiency of coupling steered molecular dynamics (SMD) with the Langmuir theory of adsorption in the low surfactant concentration regime, to predict the adsorption isotherm of sodium-dodecyl-sulfate (SDS) on bare α-alumina NPs suspended in aqueous solution. The resulting adsorption free energy landscapes (FELs) are also investigated by tuning the percentage of SDS molecules coating the target bare NP. Our findings shed light on the competing role of enthalpic and entropic interaction contributions. On one hand, the adsorption is highly promoted by the tail-NP and tail-tail nonbonded interaction adhesion; on the other hand, our results unveil the entropic nature of water and surfactant steric effects occurring at the NP surface and preventing the adsorption. Finally, a thorough analysis on the steering works emphasizes the role of the NP curvature in the FEL of adsorption. In particular, we show that, moving from a solid infinite flat surface to a nanoscale particle, a deviation from a Markovian dynamics of adsorption occurs in close proximity to a curved solid-liquid interface. Here, both the NP curvature effect and nanoscale morphology promote a modification of the thermodynamics state of adsorption with a consequent splitting of the free energy profiles and the identification of specific sites of adsorption. The modeling framework suggested in this Article provides physical insights in the surfactant adsorption onto spherical NPs and suggests some guidelines to rationally design stable NP suspensions in aqueous solutions.

Adsorption of Stearic Acid at the Iron Oxide/Oil Interface: Theory, Experiments, and Modeling

Improved friction performance is an important objective of equipment manufacturers for meeting improved energy efficiency demands. The addition of friction-reducing additives, or friction modifiers (FMs), to lubricants is a key part of the strategy. The performance of these additives is related to their surface activity and their ability to form adsorbed layers on the metal surface. However, the extent of surface coverage (mass per unit area) required for effective friction reduction is currently unknown. In this article, we show that full coverage is not necessary for significant friction reduction. We first highlight various features of surface adsorption that can influence the surface coverage, packing, and free energy of adsorption of organic FMs on iron oxide surfaces. Using stearic acid in heptane and hexadecane as model lubricant formulations, we employ a combination of experiments and molecular dynamics (MD) simulations to show how the dimerization of acid molecules in the bulk solvent and the crystallographic orientation of the surface modifies surface adsorption. In addition, we show that the solvent can strongly influence the adsorption kinetics, and MD simulations reveal that hexadecane tends to align on the surface, increasing the energy barrier for the adsorption of stearic acid to the surface. Furthermore, we present a combined approach using MD and molecular thermodynamic theory to calculate adsorption isotherms for stearic acid on iron oxide surfaces, which agrees well with experimental data obtained with a quartz crystal microbalance (QCM). Our results suggest that while the friction of systems lubricated with organic FMs decreases with increasing coverage, complete coverage of the surface is neither practically achievable nor necessary for effective friction reduction for the systems and conditions studied here.

Best Practices for Foundations in Molecular Simulations [Article v1.0]

This document provides a starting point for approaching molecular simulations, guiding beginning practitioners to what issues they need to know about before and while starting their first simulations, and why those issues are so critical. This document makes no claims to provide an adequate introduction to the subject on its own. Instead, our goal is to help people know what issues are critical before beginning, and to provide references to good resources on those topics. We also provide a checklist of key issues to consider before and while setting up molecular simulations which may serve as a foundation for other best practices documents.

Surfactant Interactions and Organization at the Gas-Water Interface (CTAB with Added Salt)

We have studied adsorbed layers of cetyltrimethylammonium bromide (CTAB) at air-water interfaces in the presence of added electrolyte. Fast bubble compression/expansion measurements were used to obtain the surface equation of state, i.e., the surface tension vs CTAB surface concentration dependence. We show that while a simple model where the surfactant molecules are assumed to be noninteracting is insufficient to describe the measured response of the surfactant layer, a modified Frumkin equation where the local interactions between the molecular components depend on their surface concentration captures the response. The variation of the effective interactions in the surfactant layer in the model shows that the interactions in the surfactant layer change from effectively repulsive to attractive with increasing surface concentration. Molecular dynamics simulations are performed to probe the origins of the change in the interactions. The simulations indicate that already at low surface concentrations the surfactants aggregate as highly dynamic rafts with surfactant orientation parallel to the interface. Increasing the concentration leads to a change in the assembly morphology at the interface: the surfactant layer thickens and the surfactants sample a range of tilted orientations with respect to the interfacial plane. The change from transient raftlike assemblies to dynamical aggregates at the interface involves a clear increase in the degree of counterion binding: we speculate that the flip of the effective interaction parameter in the model used to interpret the experimental results could result from this. The work here presents basic steps toward a proper understanding of the molecular organization and interactions of surfactants at an air-water interface. This is crucially important in understanding macroscopic properties of surfactant-stabilized systems such as foams, emulsions, and colloidal dispersions.

Molecular Structure Inhibiting Synergism in Charged Surfactant Mixtures: An Atomistic Molecular Dynamics Simulation Study

Synergistic and nonsynergistic surfactant-water mixtures of sodium dodecyl sulfate (SDS), lauryl betaine (C12B), and cocoamidopropyl betaine (CAPB) systems are studied using molecular simulation to understand the role of interactions among headgroups, tailgroups, and water on structural and thermodynamic properties at the air-water interface. SDS is an anionic surfactant, while C12B and CAPB are zwitterionic; CAPB differs from C12B by an amide group in the tail. While the lowest surface tensions at high surface concentrations in the SDS-C12B synergistic system could not be reproduced by simulation, estimated partitioning between surface and bulk shows trends consistent with synergism. Structural analysis shows the influence of the SDS headgroup pulling C12B to the surface, resulting in closely packed structures compared to their respective homomolecular-surfactant systems. The SDS-CAPB system, on the other hand, is nonsynergistic when the surfactants are mixed on account of the tilted structure of the CAPB tail. The translational excess entropy due to the tailgroup interactions discriminates between the synergistic and nonsynergistic systems. The implications of such interactions on surfactant effects in complex, multicomponent atmospheric aerosols are discussed.

Discrete Fractional Component Monte Carlo Simulation Study of Dilute Nonionic Surfactants at the Air-Water Interface

We present a newly developed Monte Carlo scheme to predict bulk surfactant concentrations and surface tensions at the air-water interface for various surfactant interfacial coverages. Since the concentration regimes of these systems of interest are typically very dilute (≪10^-5 mol. frac.), Monte Carlo simulations with the use of insertion/deletion moves can provide the ability to overcome finite system size limitations that often prohibit the use of modern molecular simulation techniques. In performing these simulations, we use the discrete fractional component Monte Carlo (DFCMC) method in the Gibbs ensemble framework, which allows us to separate the bulk and air-water interface into two separate boxes and efficiently swap tetraethylene glycol surfactants C₁₀E₄ between boxes. Combining this move with preferential translations, volume biased insertions, and Wang-Landau biasing vastly enhances sampling and helps overcome the classical "insertion problem", often encountered in non-lattice Monte Carlo simulations. We demonstrate that this methodology is both consistent with the original molecular thermodynamic theory (MTT) of Blankschtein and co-workers, as well as their recently modified theory (MD/MTT), which incorporates the results of surfactant infinite dilution transfer free energies and surface tension calculations obtained from molecular dynamics simulations.